Multifractal Company Market: An Application to the Stock Market Indices

Using the multiscale normalized partition function, we exploit the multifractal analysis based on directly measurable shares of companies in the market. We present evidence that markets of competing firms are multifractal/multiscale. We verified this by (i) using our model that described the critical properties of the company market and (ii) analyzing a real company market defined by the S&P500 index. As the valuable reference case, we considered a four-group market model that skillfully reconstructs this index’s empirical data. We point out that a four-group company market organization is universal because it can perfectly describe the essential features of the spectrum of dimensions, regardless of the analyzed series of shares. The apparent differences from the empirical data appear only at the level of subtle effects.

Toward a broader concept of societal value: family spillovers in Alzheimer's disease

Recognizing that the “healthcare sector perspective” can be too limited in some situations, the National Institute of Health and Care Excellence (NICE), Institute for Clinical and Economic Review (ICER), and the U.S. Second Panel on Cost-Effectiveness in Health and Medicine all recommend a “societal” perspective in “reference case” cost-effectiveness analyses (CEAs). Although costs of informal caregiving are sometimes included in the CEAs of Alzheimer's Disease (AD) drugs, the benefits and disutility to family members, referred to as “family spillovers” by the U.S. Second Panel, are usually omitted. We estimate that the aggregate cost of family spillovers could be substantial in the USA—on the order of USD 57 billion or over 10 percent of the total economic burden of AD in 2020. Incorporation of family spillovers in AD value frameworks and HTAs is important for comprehensively defining, rewarding, and providing high-value care in AD.

A Comparison of Alternative Fuels for Shipping in Terms of Lifecycle Energy and Cost

Decarbonization of the shipping sector is inevitable and can be made by transitioning into low- or zero-carbon marine fuels. This paper reviews 22 potential pathways, including conventional Heavy Fuel Oil (HFO) marine fuel as a reference case, “blue” alternative fuel produced from natural gas, and “green” fuels produced from biomass and solar energy. Carbon capture technology (CCS) is installed for fossil fuels (HFO and liquefied natural gas (LNG)). The pathways are compared in terms of quantifiable parameters including (i) fuel mass, (ii) fuel volume, (iii) life cycle (Well-To-Wake—WTW) energy intensity, (iv) WTW cost, (v) WTW greenhouse gas (GHG) emission, and (vi) non-GHG emissions, estimated from the literature and ASPEN HYSYS modelling. From an energy perspective, renewable electricity with battery technology is the most efficient route, albeit still impractical for long-distance shipping due to the low energy density of today’s batteries. The next best is fossil fuels with CCS (assuming 90% removal efficiency), which also happens to be the lowest cost solution, although the long-term storage and utilization of CO2 are still unresolved. Biofuels offer a good compromise in terms of cost, availability, and technology readiness level (TRL); however, the non-GHG emissions are not eliminated. Hydrogen and ammonia are among the worst in terms of overall energy and cost needed and may also need NOx clean-up measures. Methanol from LNG needs CCS for decarbonization, while methanol from biomass does not, and also seems to be a good candidate in terms of energy, financial cost, and TRL. The present analysis consistently compares the various options and is useful for stakeholders involved in shipping decarbonization.

Artificial Intelligence for Infill Well Placement and Design Optimization in Multi-layered/stacked Reservoirs Under Subsurface Uncertainty

Effective well placement and design planning accounts for subsurface uncertainties to estimate production and economic outcomes. Reservoir modelling and simulation workflows build on ensemble approaches to manage uncertainties for production forecasting. Ensemble generation and interpretation requires a higher degree of automation analytics and artificial intelligence for fast value extraction and decision support. This work develops practical intelligent workflow steps for a robust infill well placement and design scenario in multi-layered/stacked reservoirs under uncertainty. Potential well targets are classified by an opportunity index defined by a combination of rock and hydrocarbon flow properties as well as connected volumes above a minimum economic volume. Unsupervised learning techniques are applied to automate the search for alternative target areas, so-called hotspot regions. Supervised machine/learning models are used to predict infill well performance based on simulated and/or past production experience. A stochastic evaluation including all ensemble cases is used to capture uncertainty. Vertical, deviated, horizontal and multilateral wells are proposed to optimally target single or connect to multiple hotspot regions under technical and economic constraints. A structured workflow design is applied to a multi-layered/stacked reservoir model. Subsurface uncertainties are described and captured by multiple model realizations, which are constrained in areas of historical wells. An infill well program for a multi-layered/stacked reservoir is defined for incremental production increase under economic constraints. This work shows how robust well location and design builds on the full ensemble of cases with a high degree of automation using analytics and machine-learning techniques. Both production and economic targets are calculated and compared to a reference case for robust solution verification and probability of success. In conclusion, an overall reservoir-driven field development strategy is required for efficient execution. However, automation is well applicable to repetitive workflow steps which includes hotspot search in an ensemble of validated reservoir models. This work presents an integrated, intelligent solution for informed decision making on infill drilling locations and refined well design. Higher degree of automation with embedded intelligence are discussed from case generation to hotspot identification. Aspects of model calibration in a producing field environment are addressed.

Effects of the Problem-Oriented Learning Model on Middle School Students’ Computational Thinking Skills in a Python Course

The rapid development of computers and technology affects modern daily life. Individuals in the digital age need to develop computational thinking (CT) skills. Existing studies have shown that programming teaching is conducive to cultivating students’ CT, and various learning models have different effects on the cultivation of CT. This study proposed a problem-oriented learning (POL) model that is closely related to programming and computational thinking. In all, 60 eighth-grade students from a middle school in China were divided into an experimental group (EG) which adopted the POL model, and a control group (CG) which adopted the lecture-and-practice (LAP) learning model. The results showed that the students who were instructed using the POL model performed better than those who were instructed using the LAP model on CT concepts, CT practices, and CT perspectives. Significant differences were found for CT concepts and CT perspectives, but not for CT practices. Findings have implications for teachers who wish to apply new learning models to facilitate students’ CT skills, and the study provides a reference case for CT training and Python programming teaching.

Unusual Luminescence of Quartz from La Sassa, Tuscany: Insights on the Crystal and Defect Nanostructure of Quartz

Quartz from La Sassa (Tuscany, Italy) presents a unique luminescence related to intrinsic and extrinsic defects in the crystal lattice due to the growth mechanisms in hydrothermal conditions. The bright fluorescence under the UV lamp was apparent to collectors since the early 1970s, and it entered the literature as a reference case of yellow-luminescent quartz. Early reports present the history of the discovery, the geological context, and preliminary luminescence measurements of the quartz nodules, suggesting various activators as potentially responsible of the peculiar luminescence effects: uranyl groups (UO22+), rare earths (Tb3+, Eu3+, Dy3+, Sm3+, Ce3+) and polycyclic aromatic compounds (PAH). Here, we report a full investigation of the La Sassa material, by a multi-analytical approach encompassing cathodoluminescence optical microscopy (OM-CL), laser-induced fluorescence (LIF), wavelength resolved thermally stimulated luminescence (WR-TSL), trace elements analysis by mass spectrometry (ICP-MS) and Raman spectroscopy (RS). The results provide a significant step forward in the interpretation of the luminescence mechanisms: the main luminescent centres are identified as alkali-compensated (mainly Li+ and Na+, K+ and H+) aluminum [AlO4/M+]0 centres substituting for Si, where the recombination of a self-trapped exciton (STE) or an electron at a nonbridging oxygen hole centre (NBOHC) are active.

Noncentral forces mediated between two inclusions in a bath of active Brownian rods

Using Brownian Dynamics simulations, we study effective interactions mediated between two identical and impermeable disks (inclusions) immersed in a bath of identical, active (self-propelled), Brownian rods in two spatial dimensions, by assuming that the self-propulsion axis of the rods may generally deviate from their longitudinal axis. When the self-propulsion is transverse (perpendicular to the rod axis), the accumulation of active rods around the inclusions is significantly enhanced, causing a more expansive steric layering (ring formation) of the rods around the inclusions, as compared with the reference case of longitudinally self-propelling rods. As a result, the transversally self-propelling rods also mediate a significantly longer ranged effective interaction between the inclusions. The bath-mediated interaction arises due to the overlaps between the active-rod rings formed around the inclusions, as they are brought into small separations. When the self-propulsion axis is tilted relative to the rod axis, we find an asymmetric imbalance of active-rod accumulation around the inclusion dimer. This leads to a noncentral interaction, featuring an anti-parallel pair of transverse force components and, hence, a bath-mediated torque on the dimer.

The Influence of Policy Risk on Swiss Wind Power Investment

Wind energy is one of the most affordable and fastest-growing sources of electricity worldwide. As a large share of wind power generation occurs in the winter season, it could make an important contribution to seasonal diversification of domestic electricity supply. However, the development of wind energy projects in Switzerland has been characterized by long and complex administrative processes, with the planning phase taking up to a decade, more than twice as long as the European average. The objective of this chapter is to quantify the risk premium that lengthy permitting processes imply for wind energy investors in Switzerland and to suggest ways to reduce policy risk. The data have been gathered through 22 confidential interviews with project developers and several cantonal permitting agencies as well as a review of federal and cantonal regulatory documents. Furthermore, a discounted cash flow model was built to compare the profitability indicators (IRR, NPV) and the levelized cost of electricity (LCOE) of a reference case to scenarios with various risks—for example, delays in the permitting process, downsizing the project, or changes in the regulatory environment such as phasing out feed-in tariffs. The model shows that the highest profitability risks are related to the availability of a feed-in tariff, but other changes in the permitting process can also have a critical impact on the project’s bottom line. The findings illustrate a significant policy risk premium in the pre-construction stage faced by wind energy project developers in Switzerland.

Experimental investigation of the air flow in a simplified underhood environment

In this paper, a simplified underhood environment is proposed to investigate the air flow distribution in a vehicle-like set-up and provide high quality measurement data that can be used for the validation of Computational Fluid Dynamic methods. The rig can be equipped with two types of front openings representative for electrified vehicles. Furthermore, it is possible to install differently shaped blockages downstream of the fan to imitate large underhood components. The distance between the blockages and the fan can be varied in longitudinal and lateral direction. The measurements are performed with Laser Doppler Anemometry at a fixed distance downstream of the fan. The results show that the lack of an upper grille opening in the configuration for a battery electric vehicle has a notable impact on the flow field in the reference case without any downstream blockage. However, the differences in the flow field between the two front designs become less when a downstream obstruction is present. The longitudinal and lateral position of the blockages have a minor impact on the flow field compared to the shape of the obstacle itself.

A Comprehensive Machine Learning Approach for Quantitatively Analyzing Development Performance and Optimization for a Heterogeneous Carbonate Reservoir in Middle East

Compared with conventional reservoir, the development efficiency of the carbonate reservoir is lower, because of the strong heterogeneity and complicated reservoir structure. How to accurately and quantitatively analyze development performance is critical to understand challenges faced, and to propose optimization plans to improve recovery. In the study, we develop a workflow to evaluate similarities and difference of well performance based on Machine Learning methods. A comprehensive Machine Learning evaluation approach for well performance is established by utilizing Principal Component Analysis (PCA) in combination with K-Means clustering. The multidimensional dataset used for analysis consists of over 15 years dynamic surveillance data of producers and static geology parameters of formation, such as oil/water/gas production, GOR, water cut (WC), porosity, permeability, thickness, and depth. This approach divides multidimensional data into several clusters by PCA and K-Means, and quantitatively evaluate the well performance based on clustering results. The approach is successfully developed to visualize (dis)similarities among dynamic and static data of heterogeneous carbonate reservoir, the optimal number of clusters of 27-dimension data is 4. This method provides a systematic framework for visually and quantitatively analyzing and evaluating the development performance of production wells. Reservoir engineers can efficiently propose targeted optimization measures based on the analysis results. This paper offers a reference case for well performance clustering and quantitative analysis and proposing optimization plans that will help engineers make better decision in similar situation.